Prediction of Urban Flooding Risks Using High-resolution Modeling and Hybrid Rainfall Data

Flooding is one of the deadliest and costliest forms of disaster in the US, and escalating climate change and rapid growth of cities are exacerbating it. Data about flood risks could help cities make flood-producing events more manageable and less detrimental. Current flood maps and forecasting techniques are limited in accounting for pluvial flooding directly caused by extreme rainfall in urban settings. Moreover, designing infrastructure towards a flood-resilient city requires planning more comprehensively as the entire urban landscape interconnects and should be considered as an integrated system. This study adopted a previously developed modeling package for the integrated drainage system across the City of Chicago, which coupled the rain-runoff processes at the fine-scale urban catchments with an average size of 5.37x10 m(2) with a finite difference-based hydrodynamic model to solve the transient flows captured and conveyed in the sewer systems. Distinct from previous studies focusing on the methodological validation on a subset of the system, this study examined the masterplan to best account for its interconnected nature with the fewest assumptions of inflow boundary conditions. City-wide flooding vulnerabilities were firstly assessed diagnostically via model simulations of pre- selected synthetic storms constructed on regional precipitation characteristics. Further, a reanalysis of a recorded flood-producing event on April 17-19, 2013, was performed using rainfall data processed from a rain gauge network and a single WSR-88D dual-polarimetric weather radar. The diverse rainfall products were further input to drive the system response considering storage capacities with and without the inclusion of Chicago's deep tunnel system as a mitigation measure. Refined predictions of city-wide flooding were mapped In peak flood depths and durations, typical resilience Indicators of Infrastructure systems for flooding.